First Steps Towards Automatically Defining the Difficulty of Maze-Based Programming Challenges

In a world where algorithms are ubiquitous, the development of computational thinking competencies is becoming progressively important among students, technology professionals, and 21st-century citizens in general. Educational games as a means of promoting computational thinking skills have gained popularity in recent years. Offering efficient educational games that promote computational thinking competencies requires personalized learning paths through adaptive difficulty. The research presented herein is a first attempt to define a difficulty function for maze-based programming challenges using log data obtained from Kodetu, which is a block-based maze game. Specifically, we conducted three studies with 9- to 16-year-old students who were asked to solve sequences of maze-based programming challenges. Using log data from these studies, we investigated the maze characteristics and the coding limitations that affect performance in the challenges and calculated the performance obtained by the participants using a fuzzy rule-based system. The results showed that the turns in a maze, the number of total steps of a maze, and the blocks provided affect student performance. Using regression analysis, we defined a difficulty function for maze-based programming challenges that considers the weights of these factors and provides a first step towards the design of adaptive learning paths for computational thinking-related educational games.

Automated summary

In a world where algorithms are increasingly important, the development of computational thinking competencies is crucial for students, technology professionals, and 21st-century citizens. Educational games have become popular tools for promoting computational thinking skills, requiring personalized learning paths with adaptive difficulty for efficiency. This research defines a difficulty function for maze-based programming challenges and takes a step towards designing adaptive learning paths for computational thinking-related educational games.